1. Field of the Invention
The present invention relates to a pattern identification apparatus capable of additionally registering a pattern to be identified, and to a control method thereof.
2. Description of the Related Art
A facial identification technique for identifying an individual's face can be given as an example of an identification technique using pattern recognition, or a technique that typically identifies an object that is a subject within image data as the same object that is a subject in a different piece of image data. As used hereinafter in this specification, “pattern identification” will refer to judging differences in individual patterns (for example, differences in people that are separate individuals). On the other hand, “pattern detection” will refer to judging items that fall into the same category without distinguishing between individuals (for example, detecting faces without distinguishing between individuals).
A method such as that described in Baback Moghaddam's Beyond Eigenfaces: Probabilistic Matching for Face Recognition (M.I.T. Media Laboratory Perceptual Computing Section Technical Report No. 443) (“Document 1” hereinafter) and Probabilistic Visual Learning for Object Representation (IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, July 1997) (“Document 2” hereinafter), can be given as an example of a facial identification technique. This features an algorithm that enables the real-time registration and additional learning of faces by replacing individual identification problems caused by faces with a two-class identification problem having feature classes called “subtracted faces”.
For example, with the generally-known facial identification that employs a support vector machine (SVM), in order to identify the faces of n people, n SVM discriminators that identify the faces of registered people and the faces of people that are not registered are necessary. SVM learning is necessary when registering the faces of people. In SVM learning, it is necessary to store large amounts of data including the faces of people to be registered, people who are already registered, and other people, which results in extremely long calculation times; thus methods that carry out calculations in advance have generally been used.
However, according to the method in Documents 1 and 2, additional learning can be rendered essentially unnecessary by replacing the individual identification problem with the two-class identification problem discussed hereinafter. The two classes are a varying feature class related to lighting variations, expression/orientation, and so on among images of the same person, or an “intra-personal class”, and a varying feature class related to images of different people, or an “extra-personal class”. Assuming that the distributions of the stated two classes are constant regardless of the specific individual, a discriminator is configured by having reduced the individual facial identification problems to identification problems of the stated two classes. A large number of images are prepared in advance, and a discriminator that identifies the varying feature class for the same person and the varying feature class for different people carries out learning. For a new registrant, it is acceptable to store only an image of the face (or the result of extracting the necessary features from the image of the face). During identification, differential features are taken from two images, and the stated discriminator judges whether the people in the images are the same person or different people. Through this, a large amount of learning, which was necessary when registering the faces of individuals using an SVM and the like, is no longer necessary, and objects can therefore be registered in real time.
However, even if the algorithm renders additional learning unnecessary, additional learning is nevertheless a useful method for improving identification capabilities. For example, even if the configuration is such that identification is carried out using the two classes of identification problems as described above, the identification capabilities can be improved through additional learning that adjusts the identification parameters so as to enable the proper identification of a newly-registered image. There is actually a higher demand now than there was before to introduce additional learning that improves the identification capabilities by adjusting the identification parameters so as to be specialized for data registered by a user. For example, there are cases, in the recognition of individuals using the faces of people, where prioritizing an improvement of the robustness with respect to the aforementioned variations leads to degradation in the identification capabilities for similar people. It is often the case in consumer devices such as digital cameras that particularly plural similar people, such as parents and children, siblings, and so on are registered. Additional learning is thought to be a useful method for improving the identification capabilities for similar people while also maintaining the robustness with respect to variations. Carrying out additional learning generates parameters specialized for the identification of people registered by a user, and particularly people that are similar, which can be assumed to improve the identification capabilities.
However, if a user who owns multiple devices carries out additional learning in the individual devices in order to improve the identification capabilities, the specifics of the additional learning will differ from device to device, and thus the identification capabilities will also differ from device to device as a result. Furthermore, if such a user wishes to have the same identification capabilities in his or her multiple devices, in order to achieve the same identification results for an identification target that has been registered in the multiple devices, it is necessary to carry out additional learning for that identification target in each device; this places a heavy burden on the user and requires a significant amount of time for learning.